Integration of the behavioral sciences with genetics necessitates the simultaneous testing of the impact of genes and environment on phenotypic outcome. A usual approach is to explicitly test for an interaction between a specific genetic variant and an environment variable (GxE), whether or not a specific a priori hypothesis exists. While generating a host of apparently significant and often high profile findings, this approach has been met with non-replication and a great deal of criticism. While these issues are discussed in depth elsewhere, we will briefly mention several pitfalls to avoid. As with all statistical model fitting, it is important to be aware of distributional assumptions. However, given awareness of the potential impact of violations of distributional assumptions (e.g., Gaussian) in environmental measures on subsequent testing of interaction effects often leads investigators to impose artificial thresholds (e.g., median splits) on environmental indicators, consequently reducing statistical power. We recommend the use of quantile normalization as an alternative approach (Irizarry et al., 2003). Some investigators attempt to rely on methods that omit the main effects of the gene and environment and model
